Kmeans clustering method for efficacy of traditional Chinese medicinal materials based on node similarity

Abstract

The invention discloses a Kmeans clustering method for the efficacy of traditional Chinese medicinal materials based on node similarity. The method comprises the following steps: collecting related traditional Chinese medicine data, and processing the data to form a prescription composition library, a medicinal material efficacy library and a channel-tropism binary table of the nature and taste ofmedicinal materials; summarizing and classifying the efficacy of the traditional Chinese medicinal materials according to 23 efficacy tables, and constructing a medicinal material efficacy matrix; constructing a prescription-medicinal material bipartite network based on the prescription composition library; calculating expected values of medicinal material pairs based on degree distribution, andtaking the expected values of the medicinal material pairs as the similarity of the traditional Chinese medicinal materials; establishing a Kmeans clustering model based on the similarity of the traditional Chinese medicinal materials; and clustering the traditional Chinese medicinal materials based on the clustering model to obtain potential effects possibly possessed by the traditional Chinese medicinal materials. According to the method, the accuracy of Kmeans clustering via a medicinal material similarity matrix can reach 0.728. Meanwhile, Kmeans is used for clustering the nature-taste channel-tropism data of traditional medicinal materials, an obtained final result is 0.646, which is about 0.08 higher; and therefore, clustering result is allowed to be more accurate through the method.

Description

technical field [0001] The invention relates to the field of computer-aided drug design, in particular to a Kmeans method for clustering the efficacy of Chinese medicinal materials based on node similarity. Background technique [0002] According to the information of prescriptions and medicinal materials in the present invention, medicinal materials with similar or identical efficacy are grouped into one category. The clustering problem is a typical partition-based problem, and the Kmeans clustering algorithm is relatively simple and commonly used among the partition-based clustering algorithms. Kmeans is an unsupervised learning algorithm, which is a method of group observation, with few adjustable parameters, fast clustering speed and simple method. However, the traditional Kmeans algorithm has two obvious disadvantages: one is that the K value of the number of clusters is difficult to control. For example, Chinese medicinal materials have many effects, and each medicina...

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Problems solved by technology

However, the traditional Kmeans algorithm has two obvious disadvantages: one is that the K value of the number of clusters is difficult to control. For example, Chinese medicinal materials have many effects, and each medicinal material may have dozens or even hundreds of effects, so the clustering quality will be very low. Difficult to guarantee; second, the calculation of the distance between samples cannot guarantee the true similarity, especially for the special data of traditional Chinese medicine

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